CN101236607A - Rapid multi- threshold value dividing method for gray-scale image - Google Patents
Rapid multi- threshold value dividing method for gray-scale image Download PDFInfo
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Abstract
The invention relates to the digital image processing technical field, and discloses a method used for a quick multi-threshold segmentation to a gray image. The method comprises the following steps of reading the gray image firstly, and calculating a gray histogram for the gray image; smoothing the obtained histogram to obtain a smooth histogram, selecting a plurality of maximum peak points from the smooth histogram and acquiring a gray value array corresponding to the maximum peak points; expanding two ends of the gray value array, making two adjacent elements in the expanded array added and divided by two to obtain a threshold required by the multi-threshold segmentation, thereby realizing the multi-threshold segmentation.
Description
Technical field
What the present invention relates to is a kind of digital image processing techniques, specifically a kind ofly is used for the method that the gray level image rapid multi-threshold value is cut apart.
Background technology
Cutting techniques is the image model base of recognition, has purposes widely, all is a hot research problem in the middle of scientific research, production.Many Threshold Segmentation can be used for discerning the target with different color or gray scale or be used for discerning the target that gray scale changes in close limit very.As in side-scan sonar Target Recognition task, shade can be used as a feature of target, but because the echo strength and the shadow region of seabed background are close, so generally speaking, the two-value partitioning algorithm is difficult to the shadow region is extracted.And present numerous many Threshold Segmentation technology are because the big reason of calculated amount is difficult to requirement of real time.
Summary of the invention
The invention provides a kind of many Threshold Segmentation that can realize gray level image, and the method that the gray level image rapid multi-threshold value is cut apart that is used for of the real-time requirement that is content with very little.
The object of the present invention is achieved like this:
(1) read gray level image to be split, and it is deposited among the two dimensional image array A, each gray values of pixel points is all in 0～255 scope;
(2) traversing graph obtains the grey level histogram of image as array A;
(3) make grey level histogram pass through a low-pass filter, obtain smoothed histogram;
(4) open up two arrays of peak value array and gray scale array, and two arrays all are initialized as 0, peak value array and grey group are preserved the gray scale of histogram peak and peak value correspondence respectively, and the element that the peak value array is identical with the gray scale array index is one to one;
(5) traversal smoothed histogram deposits the gray scale that newly obtains peak value and peak value correspondence in peak value array and gray scale array last respectively, then with peak value array and gray scale array sort;
(6) the gray scale array of utilizing step (5) to obtain is determined each threshold value of many Threshold Segmentation;
(7) traversing graph is as array A, and each threshold ratio that obtains in each element and the step (6) among the A obtains a label, deposits the corresponding position of two-dimentional label array B in, realizes that the rapid multi-threshold value of gray level image is cut apart.
The present invention can also comprise some features like this:
1. peak value array described in the step (4) and gray scale array size equal the preset threshold value number n.
2. the ordering described in the step (5), peak value array are by the direct method ordering, and the gray scale array is made corresponding the change according to the variation of peak value array, make peak value corresponding gray scale value constant.
3. each threshold value determination method of the many Threshold Segmentation described in the step (6) is for to carry out descending sort with the gray scale array, take out preceding n-1 element in the gray scale array then, its the right and left is expanded, the extensible element on the left side is 0, the extensible element on the right is 255, obtain a new one-dimension array C, first left element from C, the adjacent element addition of this element and its right just obtains the threshold value array D that requires divided by 2 again, this process lasts till n the element of array C always, and each element among the array D is a n threshold value of requirement.
The present invention is based on many Threshold Segmentation of grey level histogram.Because multiobject existence, grey level histogram has multimodal, and therefore the some corresponding gray scale is one as threshold value and more reasonably estimates among adjacent two peaks, and this is the core concept of this gray level image rapid multi-threshold value dividing method just also.
Experiment shows, be of a size of 330 * 244 gray level image for a width of cloth, at a CPU is AMD1600+, operating system is Windows2000, programmed environment is under the condition of VC6.0, carry out 10 milliseconds consuming time altogether of 2 Threshold Segmentation, 3 Threshold Segmentation, 4 Threshold Segmentation and 5 Threshold Segmentation simultaneously, can reach the real-time requirement fully.And other many thresholding algorithms do not provide the data of the execution time aspect of algorithm at present.
Description of drawings
Fig. 1 is a FB(flow block) of the present invention.
Fig. 2 is the array C described in the step 6 of the present invention.
Fig. 3 is the array D described in the step 6 of the present invention.
Fig. 4 is a gray level image of 330 * 244 that is of a size of to be split.
Fig. 5 is with the binary segmentation result of Otsu method to Fig. 4.
Fig. 6 utilizes the four Threshold Segmentation results of the present invention to Fig. 4.
Embodiment
The invention will be further described for example below in conjunction with accompanying drawing.
1. read gray level image to be split (Fig. 4), and it is deposited among the two dimensional image array A, each gray values of pixel points is all in 0～255 scope.
2. traversing graph obtains the grey level histogram of image as array A.
3. make grey level histogram pass through a low-pass filter, obtain smoothed histogram.
4. open up two arrays---peak value array and gray scale array, and two arrays all are initialized as 0.Peak value array and grey group are preserved the gray scale of histogram peak and peak value correspondence respectively, and the element that the peak value array is identical with the gray scale array index is one to one, and peak value array and gray scale array size should equal the preset threshold value number n.
5. the traversal smoothed histogram deposits the gray scale that newly obtains peak value and peak value correspondence in peak value array and gray scale array last respectively, then with peak value array and gray scale array sort.The peak value array is by the direct method ordering, and the gray scale array is made corresponding the change according to the variation of peak value array, make peak value corresponding gray scale value constant.
6. the gray scale array is carried out descending sort, take out preceding n-1 element in the gray scale array then, this n-1 element the right and left expanded, the extensible element on the left side is 0, the extensible element on the right is 255, obtain a new one-dimension array C (as shown in Figure 2), this array comprises n+1 element, from the first left element, this element and its right adjacent element addition have just obtained the threshold value array D (as shown in Figure 3) that requires divided by 2 again, and this process lasts till n the element of array C always.Comprise n element among the array D altogether, this n element is exactly a n threshold value of requirement.
7. traversing graph is as array A, and each threshold ratio among the A among each element and the D obtains a label, deposit the corresponding position of two-dimentional label array B in, thereby the rapid multi-threshold value of having realized gray level image is cut apart.Wherein, each element is D[0 among the D] ... D[n-1], when the element among the A smaller or equal to D[k] time (k=0,1 ... n-1), then in B correspondence position give label k, when element among the A greater than D[n-1] time, then correspondence position is given label n in B, thereby realizes the many Threshold Segmentation to image.
Claims (5)
1. one kind is used for the method that the gray level image rapid multi-threshold value is cut apart, and it is characterized in that comprising the steps:
(1) read gray level image to be split, and it is deposited among the two dimensional image array A, each gray values of pixel points is all in 0～255 scope;
(2) traversing graph obtains the grey level histogram of image as array A;
(3) make grey level histogram pass through a low-pass filter, obtain smoothed histogram;
(4) open up two arrays of peak value array and gray scale array, and two arrays all are initialized as 0, peak value array and grey group are preserved the gray scale of histogram peak and peak value correspondence respectively, and the element that the peak value array is identical with the gray scale array index is one to one;
(5) traversal smoothed histogram deposits the gray scale that newly obtains peak value and peak value correspondence in peak value array and gray scale array last respectively, then with peak value array and gray scale array sort;
(6) the gray scale array of utilizing step (5) to obtain is determined each threshold value of many Threshold Segmentation;
(7) traversing graph is as array A, and each threshold ratio that obtains in each element and the step (6) among the A obtains a label, deposits the corresponding position of two-dimentional label array B in, realizes that the rapid multi-threshold value of gray level image is cut apart.
2. according to claim 1ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: peak value array described in the step (4) and gray scale array size equal the preset threshold value number n.
3. according to claim 1 and 2ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: the ordering described in the step (5), the peak value array is by the direct method ordering, and the gray scale array is made corresponding the change according to the variation of peak value array, make peak value corresponding gray scale value constant.
4. according to claim 1 and 2ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: each threshold value determination method of the many Threshold Segmentation described in the step (6) is for to carry out descending sort with the gray scale array, take out preceding n-1 element in the gray scale array then, its the right and left is expanded, the extensible element on the left side is 0, the extensible element on the right is 255, obtain a new one-dimension array C, first left element from C, the adjacent element addition of this element and its right just obtains the threshold value array D that requires divided by 2 again, this process lasts till n the element of array C always, and each element among the array D is a n threshold value of requirement.
5. according to claim 3ly be used for the method that the gray level image rapid multi-threshold value is cut apart, it is characterized in that: each threshold value determination method of the many Threshold Segmentation described in the step (6) is for to carry out descending sort with the gray scale array, take out preceding n-1 element in the gray scale array then, its the right and left is expanded, the extensible element on the left side is 0, the extensible element on the right is 255, obtain a new one-dimension array C, first left element from C, the adjacent element addition of this element and its right just obtains the threshold value array D that requires divided by 2 again, this process lasts till n the element of array C always, and each element among the array D is a n threshold value of requirement.
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| CN101887584A (en) * | 2010-07-07 | 2010-11-17 | 清华大学 | Fitness random search behavior-based multi-threshold image segmentation method |
| CN102663390A (en) * | 2012-04-28 | 2012-09-12 | 长春迪瑞医疗科技股份有限公司 | Flow-cytometry microscopic image binaryzation method |
| CN102819841A (en) * | 2012-07-30 | 2012-12-12 | 中国科学院自动化研究所 | Global threshold partitioning method for partitioning target image |
| CN102843492A (en) * | 2012-08-10 | 2012-12-26 | 北京航空航天大学 | Overlap scanning and image separating method based on transmission scanning |
| CN102915538A (en) * | 2012-09-18 | 2013-02-06 | 成都金盘电子科大多媒体技术有限公司 | Automatic binary image threshold obtaining method based on biological vision |
| CN103295226A (en) * | 2013-04-25 | 2013-09-11 | 哈尔滨工程大学 | Unsupervised sonar image segmentation method based on MRF model |
| CN104243820A (en) * | 2014-09-03 | 2014-12-24 | 奇瑞汽车股份有限公司 | Method and device for determining boundary of image |
| CN104318575A (en) * | 2014-11-04 | 2015-01-28 | 江西理工大学 | Multi-threshold image segmentation method based on comprehensive learning differential evolution algorithm |
| CN105701807A (en) * | 2016-01-12 | 2016-06-22 | 西北工业大学 | Image segmentation method based on voting strategy |
| CN106327497A (en) * | 2016-08-29 | 2017-01-11 | 湖南文理学院 | Gray-scale image threshold segmentation method based on super-extensive entropy |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101887584A (en) * | 2010-07-07 | 2010-11-17 | 清华大学 | Fitness random search behavior-based multi-threshold image segmentation method |
| CN102663390A (en) * | 2012-04-28 | 2012-09-12 | 长春迪瑞医疗科技股份有限公司 | Flow-cytometry microscopic image binaryzation method |
| CN102663390B (en) * | 2012-04-28 | 2014-04-30 | 长春迪瑞医疗科技股份有限公司 | Flow-cytometry microscopic image binaryzation method |
| CN102819841A (en) * | 2012-07-30 | 2012-12-12 | 中国科学院自动化研究所 | Global threshold partitioning method for partitioning target image |
| CN102819841B (en) * | 2012-07-30 | 2015-01-28 | 中国科学院自动化研究所 | Global threshold partitioning method for partitioning target image |
| CN102843492A (en) * | 2012-08-10 | 2012-12-26 | 北京航空航天大学 | Overlap scanning and image separating method based on transmission scanning |
| CN102915538A (en) * | 2012-09-18 | 2013-02-06 | 成都金盘电子科大多媒体技术有限公司 | Automatic binary image threshold obtaining method based on biological vision |
| CN102915538B (en) * | 2012-09-18 | 2016-05-18 | 成都金盘电子科大多媒体技术有限公司 | A kind of bianry image automatic Selection of Image Threshold based on biological vision |
| CN103295226A (en) * | 2013-04-25 | 2013-09-11 | 哈尔滨工程大学 | Unsupervised sonar image segmentation method based on MRF model |
| CN103295226B (en) * | 2013-04-25 | 2016-05-04 | 哈尔滨工程大学 | A kind of non-supervisory sonar image dividing method based on MRF model |
| CN104243820B (en) * | 2014-09-03 | 2018-02-16 | 奇瑞汽车股份有限公司 | A kind of method and device for determining image boundary |
| CN104243820A (en) * | 2014-09-03 | 2014-12-24 | 奇瑞汽车股份有限公司 | Method and device for determining boundary of image |
| CN104318575A (en) * | 2014-11-04 | 2015-01-28 | 江西理工大学 | Multi-threshold image segmentation method based on comprehensive learning differential evolution algorithm |
| CN104318575B (en) * | 2014-11-04 | 2017-02-15 | 江西理工大学 | Multi-threshold image segmentation method based on comprehensive learning differential evolution algorithm |
| CN105701807A (en) * | 2016-01-12 | 2016-06-22 | 西北工业大学 | Image segmentation method based on voting strategy |
| CN105701807B (en) * | 2016-01-12 | 2019-05-10 | 西北工业大学 | A kind of image partition method based on temporal voting strategy |
| CN106327497A (en) * | 2016-08-29 | 2017-01-11 | 湖南文理学院 | Gray-scale image threshold segmentation method based on super-extensive entropy |
| CN111145130A (en) * | 2019-12-06 | 2020-05-12 | Oppo广东移动通信有限公司 | Image processing method and device and storage medium |
| CN111145130B (en) * | 2019-12-06 | 2023-05-30 | Oppo广东移动通信有限公司 | Image processing method and device and storage medium |
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